Scan Context (SC)[1]} is one of the most popular hand-crafted global descriptors. SC is a signature-based global descriptor that directly records the vertical information of the 3D space and does not require prior knowledge. In SC, one point cloud is encoded by the distribution of cell maximum heights. Using the height rather than the shape of the structure eliminates variations in sparsity effected by sensing resolution, distance, and different scales. Variants of Scan Context have been proposed in recent years. [2]}[3]} show the feasibility by using the intensity or relative distance of 3D points.
Wang[4]} shows that a binary signature image can be obtained for each point cloud after several LoG-Gabor ﬁltering and thresholding operations on the LiDAR-Iris image representation. M2DP[5]} projects one point cloud onto several specific two-dimensional planes and generates histograms, which are combined to form a matrix. Then Singular Value Decomposition (SVD) is applied to this matrix, and the first left and right singular vectors are concatenated as the final descriptor. This method does not need to calculate point cloud normals and has high efficiency and robustness.
